Since its introduction by Eukleídēs [1]} in \(\sim \)300BC, Euclidean/L2 distance has widely been used to express distances between points. The simplicity, robustness, and mathematical convenience of this metric makes it a popular choice as a similarity/distance metric in computer vision and machine learning in the form of mean squared error (MSE) [2]}, [3]}, [4]}, where it finds wide use for all domains of data types including images [5]}, [6]}.
In this work, we present an alternate to MSE for structured data types (images) in the form of a proximally sensitive error function.
